DIFFUSION TENSOR IMAGING Marija Cauchi and Kenji Yamamoto

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DIFFUSION TENSOR IMAGING Marija Cauchi and Kenji Yamamoto

DIFFUSION TENSOR IMAGING Marija Cauchi and Kenji Yamamoto

Overview Introduction Pulse gradient spin echo ADC/DWI Diffusion tensor matrix Tractography

Overview Introduction Pulse gradient spin echo ADC/DWI Diffusion tensor matrix Tractography

DTI • Non invasive way of understanding brain structural connectivity • Macroscopic axonal organization

DTI • Non invasive way of understanding brain structural connectivity • Macroscopic axonal organization • Contrast based on the directional rate of diffusion of water molecules

DTI WATER protons = signal in DTI Diffusion property of water molecules (D) D

DTI WATER protons = signal in DTI Diffusion property of water molecules (D) D = diffusion constant Move by Brownian motion / Random thermal motion • Image intensities inversely related to the relative mobility of water molecules in tissue and the direction of the motion • •

Brownian motion of water molecule Rosenbloom et al

Brownian motion of water molecule Rosenbloom et al

DIFFUSION

DIFFUSION

Pulsed Gradient Spin-echo

Pulsed Gradient Spin-echo

ω=ϒB • ω = angular frequency • ϒ = gyromagnetic ratio • B =

ω=ϒB • ω = angular frequency • ϒ = gyromagnetic ratio • B = (B 0 + G * distance) = magnitude of the magnetic field

What is b? • b-value gives the degree of diffusion weighting and is related

What is b? • b-value gives the degree of diffusion weighting and is related to the strength and duration of the pulse gradient as well as the interval between the gradients • b changes by lengthening the separation of the 2 gradient pulses more time for water molecules to move around more signal loss (imperfect rephasing) • • • G= gradient amplitude δ = duration = trailing to leading edge separation

Apparent Diffusion Coefficient ADC – less barriers ADC - more barriers • • ln(S)

Apparent Diffusion Coefficient ADC – less barriers ADC - more barriers • • ln(S) S b-value

ADC • Dark regions – water diffusing slower – more obstacles to movement OR

ADC • Dark regions – water diffusing slower – more obstacles to movement OR increased viscosity • Bright regions – water diffusing faster • Intensity of pixels proportional to extent of diffusion • Left MCA stroke: www. radiopaedia. org

DWI • Bright regions – decreased water diffusion • Dark regions – increased water

DWI • Bright regions – decreased water diffusion • Dark regions – increased water diffusion www. radiopaedia. org

DWI ADC Hygino da Cruz Jr, Neurology 2008

DWI ADC Hygino da Cruz Jr, Neurology 2008

Colour FA map • Colour coding of the diffusion data according to the principal

Colour FA map • Colour coding of the diffusion data according to the principal direction of diffusion • red - transverse axis (x-axis) • blue – superior-inferior (z -axis) • green – anterior-posterior axis (y-axis) • Intensity of the colour is proportional to the fractional anisotropy

Water diffusion in brain tissue • Depends upon the environment: - Proportion of intracellular

Water diffusion in brain tissue • Depends upon the environment: - Proportion of intracellular vs extracellular water: cytotoxic vs vasogenic oedema - Extracellular structures/large molecules particularly in disease states - Physical orientation of tissue e. g. nerve fibre direction

Diffusion anisotropy Diffusion is greater in the axis parallel to the orientation of the

Diffusion anisotropy Diffusion is greater in the axis parallel to the orientation of the nerve fibre Diffusion is less in the axis perpendicular to the nerve fibre

Effect of Varying Gradient direction DWI z DWI x DWI y

Effect of Varying Gradient direction DWI z DWI x DWI y

What is the diffusion tensor? • In the case of anisotropic diffusion: we fit

What is the diffusion tensor? • In the case of anisotropic diffusion: we fit a model to describe our data: TENSOR MODEL - This characterises diffusion in which the displacement of water molecules per unit time is not the same in all directions

What is the diffusion tensor? Johansen-Berg et al. Ann Rev. Neurosci 32: 75 -94

What is the diffusion tensor? Johansen-Berg et al. Ann Rev. Neurosci 32: 75 -94 (2009)

What is the diffusion tensor matrix? • This is a 3 x 3 symmetrical

What is the diffusion tensor matrix? • This is a 3 x 3 symmetrical matrix which characterises the displacement in three dimensions :

The Tensor Matrix S=S e (-b. D) For a single diffusion coefficient, signal= 0

The Tensor Matrix S=S e (-b. D) For a single diffusion coefficient, signal= 0 For the tensor matrix= (-bxx. Dxx-2 bxy. Dxy-2 bxz. Dxz-byy. Dyy-2 byz. Dyz-bzz. Dzz) S = S 0 e S/S 0 =

`Diffusion MRI` Johansen-Berg and Behrens

`Diffusion MRI` Johansen-Berg and Behrens

Eigenvectors and Eigenvalues • The tensor matrix and the ellipsoid can be described by

Eigenvectors and Eigenvalues • The tensor matrix and the ellipsoid can be described by the: 1. Size of the principles axes = Eigenvalue 2. Direction of the principles axes = Eigenvector • These are represented by

The Tensor Matrix • λ 1, λ 2 and λ 3 are termed the

The Tensor Matrix • λ 1, λ 2 and λ 3 are termed the diagonal values of the tensor • λ 1 indicates the value of maximum diffusivity or primary eigenvalue (longitudinal diffusivity) • λ 2 and λ 3 represent the magnitude of diffusion in a plane transverse to the primary one (radial diffusivity) and they are also linked to eigenvectors that are orthogonal to the primary one

Indices of Diffusion Simplest method is the MEAN DIFFUSIVITY (MD): MD = <l> =

Indices of Diffusion Simplest method is the MEAN DIFFUSIVITY (MD): MD = <l> = l 1+l 2+l 3 3 - This is equivalent to the orientationally averaged mean diffusivity

Indices of Anisotropic Diffusion • Fractional anisotropy (FA): The calculated FA value ranges from

Indices of Anisotropic Diffusion • Fractional anisotropy (FA): The calculated FA value ranges from 0 – 1 : FA= 0 → Diffusion is spherical (i. e. isotropic) FA= 1 → Diffusion is tubular (i. e. anisotropic)

Colour FA Map Demonstrates the direction of fibres

Colour FA Map Demonstrates the direction of fibres

Tractography - Overview • Not actually a measure of individual axons, rather the data

Tractography - Overview • Not actually a measure of individual axons, rather the data extracted from the imaging data is used to infer where fibre tracts are • Voxels are connected based upon similarities in the maximum diffusion direction • Johansen-Berg et al. Ann Rev. Neurosci 32: 75 -94 (2009)

Tractography – Techniques Degree of anisotropy Nucifora et al. Radiology 245: 2 (2007) Streamline

Tractography – Techniques Degree of anisotropy Nucifora et al. Radiology 245: 2 (2007) Streamline tractography Probabilistic tractography

Streamline (deterministic) tractography • Connects neighbouring voxels from user defined voxels (SEED REGIONS) e.

Streamline (deterministic) tractography • Connects neighbouring voxels from user defined voxels (SEED REGIONS) e. g. M 1 for the CST • User can define regions to restrict the output of a tract e. g. internal capsule for the CST • Tracts are traced until termination criteria are met (e. g. anisotropy drops below a certain level or there is an abrupt angulation)

Probabilistic tractography • Value of each voxel in the map = the probability the

Probabilistic tractography • Value of each voxel in the map = the probability the voxel is included in the diffusion path between the ROIs • Run streamlines for each voxel in the seed ROI • Provides quantitative probability of connection at each voxel • Allows tracking into regions where there is low anisotropy e. g. crossing or kissing fibres

Crossing/Kissing fibres Crossing fibres Kissing fibres Low FA within the voxels of intersection

Crossing/Kissing fibres Crossing fibres Kissing fibres Low FA within the voxels of intersection

Crossing/Kissing fibres Assaf et al J Mol Neurosci 34(1) 51 -61 (2008)

Crossing/Kissing fibres Assaf et al J Mol Neurosci 34(1) 51 -61 (2008)

DTI - Tracts Corticospinal Tracts - Streamline Nucifora et al. Radiology 245: 2 (2007)

DTI - Tracts Corticospinal Tracts - Streamline Nucifora et al. Radiology 245: 2 (2007) Corticospinal Tracts -Probabilistic